





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "gptj"
 lowercase_						=     {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(self							:  List[Any]			,					UpperCAmelCase_							:  Dict=50_400			,					UpperCAmelCase_							:  Any=2_048			,					UpperCAmelCase_							:  Dict=4_096			,					UpperCAmelCase_							:  Dict=28			,					UpperCAmelCase_							:  Dict=16			,					UpperCAmelCase_							:  int=64			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  Tuple="gelu_new"			,					UpperCAmelCase_							:  Tuple=0.0			,					UpperCAmelCase_							:  int=0.0			,					UpperCAmelCase_							:  int=0.0			,					UpperCAmelCase_							:  Union[str, Any]=1E-5			,					UpperCAmelCase_							:  List[Any]=0.02			,					UpperCAmelCase_							:  Dict=True			,					UpperCAmelCase_							:  Optional[Any]=50_256			,					UpperCAmelCase_							:  List[str]=50_256			,					UpperCAmelCase_							:  str=False			,					**UpperCAmelCase_							:  Optional[int]			,					)			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =vocab_size
   lowerCamelCase__: Union[str, Any]         =n_positions
   lowerCamelCase__: int         =n_embd
   lowerCamelCase__: Union[str, Any]         =n_layer
   lowerCamelCase__: Optional[Any]         =n_head
   lowerCamelCase__: str         =n_inner
   lowerCamelCase__: int         =rotary_dim
   lowerCamelCase__: List[str]         =activation_function
   lowerCamelCase__: int         =resid_pdrop
   lowerCamelCase__: Any         =embd_pdrop
   lowerCamelCase__: Union[str, Any]         =attn_pdrop
   lowerCamelCase__: List[Any]         =layer_norm_epsilon
   lowerCamelCase__: Optional[Any]         =initializer_range
   lowerCamelCase__: Optional[Any]         =use_cache

   lowerCamelCase__: Any         =bos_token_id
   lowerCamelCase__: Tuple         =eos_token_id

   super().__init__(
       bos_token_id=UpperCAmelCase_			,					eos_token_id=UpperCAmelCase_			,					tie_word_embeddings=UpperCAmelCase_			,					**UpperCAmelCase_)



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def __init__(self							:  Union[str, Any]			,					UpperCAmelCase_							:  PretrainedConfig			,					UpperCAmelCase_							:  str = "default"			,					UpperCAmelCase_							:  List[PatchingSpec] = None			,					UpperCAmelCase_							:  bool = False			,					)			->List[Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_			,					task=UpperCAmelCase_			,					patching_specs=UpperCAmelCase_			,					use_past=UpperCAmelCase_)
   if not getattr(self._config			,					"pad_token_id"			,					UpperCAmelCase_):
     # TODO: how to do that better?
     lowerCamelCase__: Optional[int]         =0

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
   if self.use_past:
     self.fill_with_past_key_values_(UpperCAmelCase_			,					direction="inputs")
     lowerCamelCase__: int         ={0: "batch", 1: "past_sequence + sequence"}
   else:
     lowerCamelCase__: List[Any]         ={0: "batch", 1: "sequence"}

   return common_inputs

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->int:



   '''simple docstring'''



   return self._config.n_layer

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->int:



   '''simple docstring'''



   return self._config.n_head

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  PreTrainedTokenizer			,					UpperCAmelCase_							:  int = -1			,					UpperCAmelCase_							:  int = -1			,					UpperCAmelCase_							:  bool = False			,					UpperCAmelCase_							:  Optional[TensorType] = None			,					)			->Mapping[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =super(UpperCAmelCase_			,					self).generate_dummy_inputs(
       UpperCAmelCase_			,					batch_size=UpperCAmelCase_			,					seq_length=UpperCAmelCase_			,					is_pair=UpperCAmelCase_			,					framework=UpperCAmelCase_)

   # We need to order the input in the way they appears in the forward()
   lowerCamelCase__: str         =OrderedDict({"input_ids": common_inputs["input_ids"]})

   # Need to add the past_keys
   if self.use_past:
     if not is_torch_available():
       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
     else:
       import torch

       lowerCamelCase__    ,   lowerCamelCase__: List[str]         =common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       lowerCamelCase__: List[Any]         =seqlen + 2
       lowerCamelCase__: Optional[int]         =(
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       lowerCamelCase__: str         =[
           (torch.zeros(UpperCAmelCase_), torch.zeros(UpperCAmelCase_)) for _ in range(self.num_layers)
       ]

   lowerCamelCase__: Dict         =common_inputs["attention_mask"]
   if self.use_past:
     lowerCamelCase__: Optional[Any]         =ordered_inputs["attention_mask"].dtype
     lowerCamelCase__: str         =torch.cat(
         [ordered_inputs["attention_mask"], torch.ones(UpperCAmelCase_			,					UpperCAmelCase_			,					dtype=UpperCAmelCase_)]			,					dim=1)

   return ordered_inputs







 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->int:



   '''simple docstring'''



   return 13


"""simple docstring"""



def 	_lowerCamelCase(	a = 1   ,    a = 1_0_0_0				):
       __a       =    1
       __a       =    0

       for divide_by_number in range(a   ,    digit + 1				):
              __a       =    []
              __a       =    numerator
              for _ in range(1   ,    digit + 1				):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(a				):
                                   __a       =    len(a				)
                                   __a       =    divide_by_number
                     else:
                            has_been_divided.append(a				)
                            __a       =    now_divide * 1_0 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()
